19 papers
EvoMem-VLA: State-Evolution Memory for Long-Horizon Robot Manipulation
Yuheng Na, Zhide Zhong, Junjie He +7
Most vision-language-action (VLA) models rely on current observations and lose task-relevant evidence once it leaves view, limiting performance on long-horizon, memory-dependent ta…
AffordanceWAM: Affordance-Aware Joint World-Action Modeling for Robot Manipulation
Jiadi You, Qize Yu, Yue Chen +11
Generalizable robot manipulation requires predicting how a scene will evolve, identifying where interactions are feasible, and determining how to act. Action-labeled robot videos d…
4D-WAM: Infusing Spatiotemporal Awareness into World Action Models through Trajectory Fields
Lishan Yang, Wenxuan Song, Xi Wang +14
Building on recent advances in world models, World Action Models (WAMs) jointly model video prediction and action generation. However, they typically represent videos in 2D pixel s…
SG-WAM: Text-Grounded and Spatial-aware Semantic Guidance for World-Action Models
Junjie He, Junfeng Li, Zhide Zhong +9
World-Action Models (WAMs) have emerged as a promising paradigm for robotic manipulation. However, most existing WAMs generate future videos and actions by relying mainly on visual…
Is Forward Prediction Enough? Physical State Grounding for JEPA World Models
Haodong Yan, Jiaguan Zhu, Mingyuan Jia +12
Learning structured and control-relevant latent representations remains a key challenge for world models. Recent JEPA-based world models learn action-conditioned predictive latent…
Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models
Haodong Yan, Junfeng Li, Junjie He +12
Mainstream World-Action Models (WAMs) adapt pretrained video generation models (VGMs) for robot control, transferring their learned dynamics prior for action prediction. These VGMs…
DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation
Junfeng Li, Junjie He, Zhide Zhong +12
Vision-Language-Action (VLA) models have become a powerful paradigm for robot manipulation, but training a single generalist policy for heterogeneous robot embodiments remains an o…
GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch
GigaWorld Team, Angen Ye, Angyuan Ma +26
World Action Models (WAMs) improve robot policy learning by jointly modeling actions and future visual observations, using future scene evolution as dense supervision for physicall…
RoboMemArena: A Comprehensive and Challenging Robotic Memory Benchmark
Huashuo Lei, Wenxuan Song, Huarui Zhang +10
Memory is a critical component of robotic intelligence, as robots must rely on past observations and actions to accomplish long-horizon tasks in partially observable environments.…
VLA-OPD: Bridging Offline SFT and Online RL for Vision-Language-Action Models via On-Policy Distillation
Zhide Zhong, Haodong Yan, Junfeng Li +3
Although pre-trained Vision-Language-Action (VLA) models exhibit impressive generalization in robotic manipulation, post-training remains crucial to ensure reliable performance dur…
DualCoT-VLA: Visual-Linguistic Chain of Thought via Parallel Reasoning for Vision-Language-Action Models
Zhide Zhong, Junfeng Li, Junjie He +10
Vision-Language-Action (VLA) models map visual observations and language instructions directly to robotic actions. While effective for simple tasks, standard VLA models often strug…
S-VAM: Shortcut Video-Action Model by Self-Distilling Geometric and Semantic Foresight
Haodong Yan, Zhide Zhong, Jiaguan Zhu +10
Video action models (VAMs) have emerged as a promising paradigm for robot learning, owing to their powerful visual foresight for complex manipulation tasks. However, current VAMs,…
DFM-VLA: Iterative Action Refinement for Robot Manipulation via Discrete Flow Matching
Jiayi Chen, Wenxuan Song, Jiaxin Fang +12
Vision-Language-Action (VLA) models that encode actions using a discrete tokenization scheme have been widely adopted for robotic manipulation, but existing decoding paradigms rema…
Rethinking the Practicality of Vision-language-action Model: A Comprehensive Benchmark and An Improved Baseline
Wenxuan Song, Jiayi Chen, Xiaoquan Sun +12
Vision-Language-Action (VLA) models have emerged as a generalist robotic agent. However, existing VLAs are hindered by excessive parameter scales, prohibitive pre-training requirem…
Open-world Hand-Object Interaction Video Generation Based on Structure and Contact-aware Representation
Haodong Yan, Hang Yu, Zhide Zhong +8
Generating realistic hand-object interactions (HOI) videos is a significant challenge due to the difficulty of modeling physical constraints (e.g., contact and occlusion between ha…
ReconVLA: Reconstructive Vision-Language-Action Model as Effective Robot Perceiver
Wenxuan Song, Ziyang Zhou, Han Zhao +7
Recent advances in Vision-Language-Action (VLA) models have enabled robotic agents to integrate multimodal understanding with action execution. However, our empirical analysis reve…
FlowVLA: Visual Chain of Thought-based Motion Reasoning for Vision-Language-Action Models
Zhide Zhong, Haodong Yan, Junfeng Li +8
Many Vision-Language-Action (VLA) models are built upon an internal world model trained via next-frame prediction ``''. However, this paradigm attempts to…
Physically-Based Photometric Bundle Adjustment in Non-Lambertian Environments
Lei Cheng, Junpeng Hu, Haodong Yan +5
Photometric bundle adjustment (PBA) is widely used in estimating the camera pose and 3D geometry by assuming a Lambertian world. However, the assumption of photometric consistency…
GazeMoDiff: Gaze-guided Diffusion Model for Stochastic Human Motion Prediction
Haodong Yan, Zhiming Hu, Syn Schmitt +1
Human motion prediction is important for many virtual and augmented reality (VR/AR) applications such as collision avoidance and realistic avatar generation. Existing methods have…